Identification of Patterns over Regional Scales Using Self-Organising Maps on Images from Marine Modelling Outputs

The Self-Organizing Feature Map (or SOM), has been used to analyse a dataset consisting of oceanographic modelling output images, in order to identify patterns in the hydrodynamic behaviour of the south-east Tasmanian (SETas) coastal region over a 360-day period between August 2009 and August 2010. The SOM provided a visualization of the dataset, distributed across a 5x7 two- dimensional grid, which enabled an oceanographer to identify significant hydrodynamic patterns being exhibited by the SETas region over that period. Four prototype (typical) states were identified by the oceanographer, who then interpreted each of these states in terms of the major ocean currents which impact on the region, the East Australian Current and the Zeehan Current. These results indicate that SOM analysis can be a useful technique for identifying patterns in large oceanographic datasets, such as those now being provided by remote sensing, ocean modelling and marine sensor network technologies.